[cogman10]

I recently had this happen from a senior engineer. What's really frustrating is I TOLD them the issues and how to fix it. Instead of listening to what I told them, they plugged it into GPT and responded with "Oh, interesting this is what GPT says" (Which, spoiler, was similar but lacking from what I'd said).

Meaning, instead of listening to a real-life expert in the company telling them how to handle the problem they ignored my advice and instead dumped the garbage from GPT.

I really fear that a number of engineers are going to us GPT to avoid thinking. They view it as a shortcut to problem solve and it isn't.

[colechristensen]

>They view it as a shortcut to problem solve and it isn't

Oh but it is, used wisely.

One: it's a replacement for googling a problem and much faster. Instead of spending half an hour or half a day digging through bug reports, forum posts, and stack overflow for the solution to a problem. LLMs are a lot faster, occasionally correct, and very often at least rather close.

Two: it's a replacement for learning how to do something I don't want to learn how to do. Case Study: I have to create a decent-enough looking static error page for a website. I could do an awful job with my existing knowledge, I could spend half a day relearning and tweaking CSS, elements, etc. etc. or I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is.

LLMs are not a replacement for real understanding, for digging into a codebase to really get to the core of a problem, or for becoming an expert in something, but in many cases I do not want to, and moreover it is a poor use of my time. Plenty of things are not my core competence or anywhere near the goals I'm trying to achieve. I just need a quick solution for a topic I'm not interested in.

[ijidak]

This exactly!

There are so many things that a human worker or coder has to do in a day and a lot of those things are non-core.

If someone is trying to be an expert on every minor task that comes across their desk, they were never doing it right.

An error page is a great example.

There is functionality that sets a company apart and then there are things that look the same across all products.

Error pages are not core IP.

At almost any company, I don't want my $200,000-300,000 a year developer mastering the HTML and CSS of an error page.

[vuserfcase]

>Oh but it is, used wisely.

Sufficiently advanced orange juice extractor is the solution to any problem. Doesen't necessarily mean you should build the sufficient part.

>One: it's a replacement for googling a problem and much faster

This is more to do with the problem that google results have gone downhill very rapidly. It used to be you could find what you were looking for very fast and solve a problem.

>I could ask an LLM to do it and then tweak the results. Five minutes for "good enough" and it really is.

When the cost of failures is low, a hackjob can be economical, like a generated picture for entertainment or a static error page. Miscreating a support for a bridge it is not very economical

[_ea1k]

I wonder if this is an indication that they didn't really understand what you said to begin with.

[colechristensen]

If I had a dollar for every time I told someone how to fix something and they did something else...

Let's just say not listening to someone and then complaining that doing something else didn't work isn't exactly new.

[silversmith]

I often do this - ask a LLM for an answer when I already have it from an expert. I do it to evaluate the ability of the LLM. Usually not in the presence of said expert tho.

[namaria]

Just using LLMs on the (few) things I have specialist knowledge of it's clear they are extremely limited. I get absurdly basic mistakes and I am very wary of even reading LLM output about topics I don't command. It's easy to get stuck on dead ends reasoning wise even by getting noisy input.

[tharant]

Is it possible that what happened was an impedance mismatch between you and the engineer such that they couldn’t grok what you told them but ChatGPT was able to describe it in a manner they could understand? Real-life experts (myself included, though I don’t claim to be an expert in much) sometimes have difficulty explaining domain-specific concepts to other folks; it’s not a flaw in anyone, folks just have different ways of assembling mental models.

[kevmo314]

Whenever someone has done that to me, it's clear they didn't read the ChatGPT output either and were sending it to me as some sort of "look someone else thinks you're wrong".

[tharant]

Again, is it possible you and the other party have (perhaps significantly) different mental models of the domain—or maybe different perspectives of the issues involved? I get that folks can be contrarian (sadly, contrariness is probably my defining trait) but it seems unlikely that someone would argue that you’re wrong by using output they didn’t read. I see impedance mismatches regularly yet folks seem often to assume laziness/apathy/stupidity/pride is the reason for the mismatch. Best advice I ever received is “Assume folks are acting rationally, with good intention, and with a willingness to understand others.” — which for some reason, in my contrarian mind, fits oddly nicely with Hanlon’s razor but I tend to make weird connections like that.

[kevmo314]

> is it possible you and the other party have (perhaps significantly) different mental models of the domain—or maybe different perspectives of the issues involved?

Yes, however typically if that's the case they will respond with some variant of "ChatGPT mentioned xyz so I started poking in that direction, does that make sense?" There is a markedly different response when people are using ChatGPT to try to understand better and that I have no issue with.

I get what you're suggesting but I don't think people are being malicious, it's more that the discussion has gotten too deep and they're exhausted so they'd rather opt out. In some cases yes it does mean the discussion could've been simplified, but sometimes when it's a pretty deep, technical reason it's hard to avoid.

A concrete example is we had to figure out a bug in some assembly code once and we were looking at a specific instruction. I didn't believe that instruction was wrong and I pointed at the docs suggesting it lined up with what we were observing it doing. Someone responded with "I asked ChatGPT and here's what it said: ..." without even a subsequent opinion on the output of ChatGPT. In fact, reading the output it basically restated what I said, but said engineer used that as justification to rewrite the instruction to something else. And at that point I was like y'know what, I just don't care enough.

Unsurprisingly, it didn't work, and the bug never got fixed because I lost interest in continuing the discussion too.

I think what you're describing does happen in good faith, but I think people also use the wall of text that ChatGPT produces as an indirect way to say "I don't care about your opinion on this matter anymore."

[cogman10]

Definitely a possibility.

However, I have a very strong suspicion they also didn't understand the GPT output.

To flush out the situation a bit further, this was a performance tuning problem with highly concurrent code. This engineer was initially tasked with the problem and they hadn't bothered to even run a profiler on the code. I did, shared my results with them, and the first action they took with my shared data was dumping a thread dump into GPT and asking it where the performance issues were.

Instead, they've simply been littering the code with timing logs in hopes that one of them will tell them what to do.

[59nadir]

I'm sorry, how is this a "senior engineer"? Is this a "they worked in the industry for 6 years and are now senior" type situation or are they an actual senior engineer? Because it seems like they're lacking the basics to work on what you yourself seem to consider senior engineer problems for your project.

Also, what is your history and position in the company? It seems odd that you'd get completely ignored by this supposed senior engineer (something that usually happens more often with overconfident juniors) if you have meaningful experience in the field and domain.

[cogman10]

> how is this a "senior engineer"? Is this a "they worked in the industry for 6 years and are now senior" type situation...

Yeah, this is the situation exactly, though I've known a few seniors that were senior just because they've hung around and not experience.

> what is your history and position in the company? It seems odd that you'd get completely ignored by this supposed senior engineer

Been with the company for over a decade at this point. I think I have a pretty good reputation generally. Someone sent me a "This is why cogman10 is the GOAT" message for some of my technical interactions on large public team chats.

Why I'm being ignored? I have a bunch of guesses but nothing I'm willing to share.

[tharant]

It sounds like the engineer may have little/no experience with concurrency; a lot of folks (myself included) sometime struggle with how various systems handle concurrency/parallelism and their side effects. Perhaps this is an opportunity for you to “show not tell” them how to do it.

But I think my point still holds—it’s not the tool that should be blamed; the engineer just needs to better understand the tool and how/when to use it appropriately.

Of course, our toolboxes just keep filling up with new tools which makes it difficult to remember how to use ‘em all.

[delusional]

Those people weren't engineers to start with.

[layer8]

Software engineers rarely are.

I’m saying this tongue in cheek, but there’s some truth to it.

[throwanem]

There is much truth. Railway engineers 'rarely were' too, once upon a time, and for in my view essentially the same reasons.

[throwanem]

You should ask yourself why this organization wants engineering advice from a chatbot more than from you.

I doubt the reason has to do with your qualities as an engineer, which must be basically sound. Otherwise why bother to launder the product of your judgment, as you described here someone doing?

[tharant]

> I really fear that a number of engineers are going to us GPT to avoid thinking. They view it as a shortcut to problem solve and it isn't.

How is this sentiment not different from my grandfather’s sentiment that calculators and computers (and probably his grandfather’s view of industrialization) are a shortcut to avoid work? From my perspective most tools are used as a shortcut to avoid work; that’s kinda the while point—to give us room to think about/work on other stuff.

[parliament32]

Because calculators aren't confidently wrong the majority of the time.

[tharant]

In my experience, and for use-cases that are carefully considered, language models are not confidently wrong a majority of the time. The trick is understanding the tool and using it appropriately—thus the “carefully considered” approach to identifying use-cases that can provide value.

[namaria]

In the very narrow fields where I have a deep understanding, LLM output is mostly garbage. It sounds plausible but doesn't stand up to scrutiny. The basics that it can regurgitate from wikipedia sound mostly fine but they are already subtly wrong as soon as they depart from stating very basic facts.

Thus I have to assume that for any topic I do not fully understand - which is the vast majority of human knowledge - it is worse than useless, it is actively misleading. I try to not even read much of what LLMs produce. I might give it some text and riff about it if I need ideas, but LLMs are categorically the wrong tool for factual content.

[tharant]

> In the very narrow fields where I have a deep understanding, LLM output is mostly garbage > Thus I have to assume that for any topic I do not fully understand - which is the vast majority of human knowledge - it is worse than useless, it is actively misleading.

Why do you have to make that assumption? An expert arborist likely won’t know much about tuning GC parameters for the JVM but that won’t make them “worse than useless” or “actively misleading” when discussing other topics, and especially not when it comes to the stuff that’s relatively tangential to their domain.

I think the difference we have is that I don’t expect the models to be experts in any domain nor do I expect them to always provide factual content; the library can provide factual content—if you know how to use it right.

[parliament32]

There's a term corollary to what you're trying to argue here: https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect

> You open the newspaper to an article on some subject you know well... You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them. In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.

[vuserfcase]

A use-case that can be carefully considered requires more knowledge about the use-case than the LLM, it requires you to understand the specific model's training and happy paths, it requires more time to make it output the thing you want than just doing it yourself. If you don't know enough about the subject or the model, you will get confident garbage

[tharant]

> A use-case that can be carefully considered requires more knowledge about the use-case than the LLM

I would tend to agree with that assertion…

> it requires you to understand the specific model's training and happy paths

But I strongly disagree with that assertion; I know nothing of commercial models’ training corpus, methodology, or even their system prompts; I only know how to use them as a tool for various use-cases.

> it requires more time to make it output the thing you want than just doing it yourself.

And I strongly disagree with that one too. As long as the thing you want it to output is rooted in relatively mainstream or well-known concepts, it’s objectively much faster than you/we are; maybe it’s more expensive but it’s also crazy fast—which is the point of all tools—and the precision/accuracy of most speedy tools can be often deferred until a later step in the process.

> If you don't know enough about the subject or the model, you will get confident garbage

Once you step outside their comfort zone (their training), well, yah… they do all tend to be unduly confident in their responses—I’d argue however that it is a trait they learned from us; we really like to be confident even when we’re wrong and that trait is borne out dramatically across the internet sources on which a lot of these models were trained.

[stevage]

Did you grandpa think that calculators made engineers worse at their jobs?

[tharant]

I don’t know for certain (he’s no longer around) but I suspect he did. The prevalence of folks who nowadays believe that Gen-AI makes everything worse suggests to me that not much has changed since his time.

I get it; I’m not an AI evangelist and I get frustrated with the slop too; Gen-AI (and many of the tools we’ve enjoyed over the past few millennia) was/is lauded as “The” singular tool that makes everything better; no tool can fulfill that role yet we always try to shoehorn our problems into a shape that fits the tool. We just need to use the correct tools for the job; in my mind, the only problem right now is that we have a really capable tool and have identified some really valuable use-cases for that tool yet we also keep trying to use it for (what I believe are, given current capabilities) use-cases that don’t fit the tool.

We’ll figure it out but, in the meantime, while I don’t like to generalize that a tech or its use-cases are objectively good/bad, I do tend to have an optimistic outlook for most tech—Gen-AI included.